Global Climate Models (GCMs) are considered the most feasible tools to estimate future climate change. The objective of this study was to assess the interpretation of 19 GCMs of Coupled Model Intercomparison Project 6 (CMIP6) in replicating the historical precipitation and temperature of climate prediction center data for the Amu Darya river basin (ADRB) and the projection of climate of the basin using the selected GCMs. The Kling Gupta efficiency (KGE) metric was used to assess the effectiveness of GCMs to simulate the annual geographic variability of precipitation, maximum and minimum temperature (Pr, Tmx and Tmn). A multi-criteria decision-making approach (MCDMA) was used to integrate the KGE values to rank GCMs. The results revealed that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR are the best in replicating observed Pr, Tmx and Tmn in ADRB. Projection of climate employing the selected GCMs indicated an increase in precipitation (9.9-12.4%) and temperature (1.3-5.5⁰C) in the basin for all the shared socioeconomic pathways (SSPs), particularly for the far future (2060-2099). A significant variation can be seen in temperature over the different climatic zone. However, the intercomparison of selected GCM projected revealed high uncertainty in the projected climate. The uncertainty is higher in the far future and higher SSPs compared to the near future and lower SSPs.
Research Article
Selection of CMIP6 GCM With Projection of Climate Over The Amu Darya River Basin
https://doi.org/10.21203/rs.3.rs-1031530/v1
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General circulation model
CMIP6
climate projection
shared socioeconomic pathway
Amu Darya river basin.
Climate change is a worldwide phenomenon that affects almost all aspects of life. The frequency and intensity of climatic hazards have increased globally, which disrupted agricultural livelihoods, water resources, food security, socioeconomic development and human health in many regions (Li et al., 2020a; Pour et al., 2020; Shiru et al., 2020; Wang et al., 2019). However, the impacts of climate change are not consistent throughout the globe. Arid and semi-arid regions are relatively more sensitive than other climate zones due to their vulnerable ecosystem (Ahmed et al., 2019c; Pour et al., 2020). Therefore, due to its arid to semi-arid environment, Central Asia is one of the most exposed areas to climate change (Yadav et al., 2019).
Climate change is generally perceived by studying precipitation and temperature variables (Sheffield and Wood, 2008). Global Climate Models (GCMs) are considered the most feasible tools for simulating the past climate and projection of future climate based on emission scenarios (Li et al., 2020a). Over the years, many GCMs have been developed, and the structure of Coupled Model Intercomparison Project (CMIPs) has been revised from CMIP1 to the newly designed CMIP6 (Meehl et al., 2014; Pascoe et al., 2019; Scher and Messori, 2019). CMIP6 is a set of GCMs and the latest released stage of the CMIP series (Eyring et al., 2016). In addition to the DECK (Diagnostic, Evaluation and Characterization of Klima experiments) and the CMIP6 historical replication, 21 individual MIPs have been endorsed by the CMIP panel for inclusion in CMIP6 (Eyring et al., 2015). The CMIP6 historical simulations consider both anthropogenic and geogenic forcing for the period 1850-2014 (Srivastava et al., 2020). The number of GCMs has increased concerning each generation of MIPs, reaching 55 in CMIP6 (Eyring et al., 2016). In comparison to CMIP5, CMIP6 has numerous advantages that combine RCPs with SSPs, improvements in the models and additional experiments, higher spatial resolution, less biases, and better representation of synoptic processes (Kamal et al., 2021; Su et al., 2021a).
Multi-model ensembles (MMEs) are usually used to minimize the GCMs uncertainty and generate outputs consistent with local (Ahmed et al., 2019a; Wang et al., 2018). Therefore, choosing a suitable set of GCM is important for better water resource management and climate change impact assessment, especially in developing nations with limited human and computational resources (Hassan et al., 2020; Lin and Tung, 2017). This is because GCMs are not free of biases, either underestimate or overestimate, although the uncertainty is smaller in CMIP6 GCMs than other CMIPs (Srivastava et al., 2020). Selected GCMs should replicate the observed climate in terms of spatial and temporal variability (Nashwan and Shahid, 2020). Generally, a preferable GCM ensemble is selected according to their past performance and the envelope method (Hassan et al., 2020; Salman et al., 2018). The past performance method examines GCMs capability to mimic the past climate without considering the future projection (Raju and Kumar, 2014; Srinivasa Raju and Nagesh Kumar, 2015). The envelope approach selects a set of GCMs models based on probable future climate projections without concerning a GCMs' performance to replicate the historical climate (Warszawski et al., 2014). However, the past performance method is most widely used for GCM ensemble member selection, considering that models able to replicate the observed climate of an area have a better ability to project its future climate (Khan et al., 2020; Nashwan and Shahid, 2020). In literature, different approaches have been offered for GCM selection and ensemble preparation (Hassan et al., 2020; Salman et al., 2018; Shiru et al., 2020).
Several studies assessed climate in central Asia (CA) at the basin, country or regional scale. Duulatov et al. (2019) used 4 GCMs of CMIP5 to assess rainfall changes over CA models for radiative concentration pathways (RCPs) 2.6 and 8.5. Gulakhmadov et al. (2020) used 5 GCMs of CMIP5 to project precipitation (Pr), maximum temperature (Tmx) and minimum temperature (Tmn) in the Vakhsh River Basin of CA for RCP4.5 and RCP8.5. Ta et al. (2018) assessed the performance of 37 GCMs of CMIP5 to mimic historical Pr over CA. Huang et al. (2014) evaluated the ability of 28 GCMs of CMIP5 to project changes in annual Pr over CA for RCPs 2.6 and 8.5. Xiong et al. (2021) assessed the skill of 24 CMIP5 models against climate research unit (CRU) historical temperature in CA. Zhao et al. (2018) used 25 CMIP5 to simulate the subtropical westerly jet and its impact on projected RCP8.5 summer rainfall over CA. Recently, CMIP6 GCMs are also used for climate projections in different parts of CA. Jiang et al. (2020) used 15 CMIP6 models to assess changes in Pr in CA for four shared socioeconomic pathways (SSPs). Li et al. (2020b) used 4 CMIP6 models to evaluate total column water vapor changes in the atmosphere over CA for different SSPs. Guo et al. (2021) assessed the ability of CMIP6 in simulating Pr over CA. Li et al. (2021) used four models of CMIP5 and CMIP6 to project future changes of biodiversity in CA for different RCPs and SSPs.
The Amu Darya River (ADR) is the primary supply of fresh water in CA, and it flows through five countries: Kyrgyzstan, Tajikistan, Afghanistan, Turkmenistan and Uzbekistan (Jalilov et al., 2016). The region's livelihood and national economy depend heavily on water supplies in the ADR basin (Saidmamatov et al., 2020). The basin is highly vulnerable to climate owing to its arid to semi-arid climate and fragile environment. However, only fewer studies evaluated climate change over the ADR basin. Hagg et al. (2013) used 6 CMIP3 GCMs to project annual and seasonal glacier changes and runoff in the upper ADR basin. White et al. (2014) assessed the ability of fourteen CMIP3 GCMs precipitation and temperature against CRU to project future water supply in the ADR basin. Lutz et al. (2013) evaluated CMIP3 and CMIP5 MME against The Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation (APHRODITE) and Princeton’s Global Meteorological Forcing Data (PGMFD) datasets to assess the impact of climate change on the future glacier extent in the ADR and Syr Darya river basins. Su et al. (2021b) developed an integrated multi-GCM Bayesian‐neural‐network hydrological study using GCM simulated climate to assess the influence of climate change on runoff in the ADR basin. Different studies in the basin and other CA regions used different sets of GCMs, which produced contradictory climate projections. This has also made decision-making based on the previous studies impossible. It is very important to recommend a suitable ensemble for climate projection in the ADR basin. Besides, reliable projection of basin's climate with the selected GCMs for SSPs is vital to aid climate mitigation policymaking.
This study aims to assess CMIP6 GCMs' ability to replicate observed climate in the ADR basin to recommend an appropriate ensemble for the basin's climate projections. Besides, the study projected the basin's climate with associated uncertainty using the selected GCMs to provide vital information required for climate change adaptation planning.
2.1. Study Area
The Amu Darya River (Figure 1) is the longest river and primary source of fresh water in CA. The river length is 2,540 km, with annual mean flow of about 79 km3 (Awan et al., 2011; Leng et al., 2021; Schlüter and Herrfahrdt-Pähle, 2011; Wegerich, 2008; Xu et al., 2021). It originated in the high glaciers and snow-covered Hindukush and Pamir mountains in Tajikistan, Kyrgyzstan and Afghanistan, and passed through the deserts and the arid plain in Uzbekistan and Turkmenistan before reaching the Aral Sea (Schlüter and Herrfahrdt-Pähle, 2011; Wang et al., 2021). The average annual rainfall of the basin is 400 mm (Salehie et al., 2021). The topography of the ADR basin ranges from 7,500 m in the upstream mountains to around 200 m in the downstream northwest plains with a delta and feeding the Aral Sea. Some locations in the upstream study area receive rainfall amount nearly 2000 mm, while only 100 mm in the downstream (Behzod and Su-Chin, 2013).
2.2. Gridded data
In this study, the Climate Prediction Center (CPC) data of Pr, Tmx and Tmn for the time horizon of 1979-2014 were used as a reference to estimate the performance of GCMs. CPC is a gauge-based gridded dataset established by the NOAA Climate Prediction Center, National Centers for Environmental Prediction (Xie et al., 2007). This dataset is available with a spatial resolution of 0.5⁰×0.5⁰ from 1979 to present at https://psl.noaa.gov/data/gridded/.
Salehie et al. (2021) assessed the ability of seven gridded precipitation datasets against 55 stations data over the Amu Darya basin. They found that CPC is the best dataset that capable of replicating the observed precipitation. Salehie et al. (2021b) also showed CPC's ability to replicate observed temperature in the basin. Therefore, the CPC dataset was considered the reference dataset used in this study to assess GCMs' performance in simulating Pr, Tmx and Tmn.
2.3. CMIP6 GCMs simulations and scenarios
Nineteen CIMP6 GCMs monthly Pr, Tmx and Tmn simulations were used to select the best GCMs to replicate the rainfall in the study area. Several international research organizations collaborated to produce these GCMs is presented in Table S-1. The GCMs were chosen based on the availability of monthly historical simulation and future projections for four SSPs, namely SSPs (1-2.6, 2-4.5, 3-7.0 and 5-8.5). For a fair comparison of various GCMs model, historical single run r1i1p1f1 (e.g., ensemble members [r1], initialization states [i1], physical parameterizations [p1] and forcing index [f1]) were considered for all cases. GCMs' historical (1979-2014) and projection (2015-2100) data could be downloaded from this website: http://cmip-pcmdi.llnl.gov/ cmip6/.
The reference dataset (CPC) and GCMs have various spatial resolutions. Hence, CPC, historical and future projection GCMs were interpolated to 73 grid points having the common 1°×1° resolution for all variables (Pr, Tmx and Tmn). This resolution was chosen as it was almost close to the mean of all GCMs. The inverse distance weighting approach was used to interpolate the four closest grid points to each grid point, as discussed in (Hamed et al., 2021). GCMs were re-gridding to prevent bias from different spatial resolutions during performance evaluation (Nashwan and Shahid, 2020). The procedures for the selection of GCMs are defined below:
- Compare the monthly historical variables of 19 GCMs and the reference dataset (CPC) using the KGE metric at each grid point.
- Rank each variable based on KGE, then select the GCM subset using the multi-criteria decision analysis (MCDMA) method.
- Create an MME using the top four GCMs results from the ranking process.
- Use the MME to project all climate variables in the near and far future.
3.1 Performance evaluation using KGE
In this study, Kling-Gupta efficiency (KGE) was used to evaluate the performance of the historical GCMs in replicating the observed dataset in three different variables (Pr, Tmx and Tmn). KGE is a statistical index incorporating correlation, bias, and variability to find the association and biases in the mean and the changes of measured and predicted climate variables (Nashwan and Shahid, 2020). KGE was presented by Gupta et al. (2009) and then modified by Kling et al. (2012), which defined as:
where r is the linear correlation between observed and gridded data; β represents the bias obtained by the ratio of mean simulated and mean observed and is the variability component can be calculated by the percentage of the simulated (GCMs) and observed (CPC) coefficients of variation:
where \(\mu\) and \(\sigma\) are the distribution mean and standard deviation, respectively, while the subscripts s and o reflect simulated and observed data, respectively. KGE can range between \(-{\infty }\) to 1, where 1 is the optimal value.
3.2 Ranking of GCMs
Multi-criteria decision making analysis (MCDMA) is a recall for evaluating an alternative from multi-objective complex problems, uncertain information of physical and social issues when no method is found to undertake (Wang et al., 2009). MCDMA approach integrates statistically and numerically obtained outcomes to rank the best option of different choices (Salehie et al., 2021). In this study, the GCMs are ranked using the KGE metric in a different variable based on their performance. The higher weight to a GCM model is provided as a higher frequency of occurrence, and therefore the models top-ranked. For example, if the model ranked first at most grids from the total number of grid points for each variable, it shall be ranked the first among all GCMs. The output rank from each variable was merged using the average method as shown in Equation 3. Finally, the average sum was ranked to describe the best GCMs among the set.
3.3 Ensemble projections
The top four GCMs were used to create an ensemble mean for climate projection. The advantages of MME are increasing the accuracy of projection and reducing uncertainty in the individual model (Ahmed et al., 2020; Salman et al., 2018; Shiru et al., 2020). Different techniques are used to generate MME, but the most commonly used is the weighted average method (Sachindra et al., 2014; Shiru et al., 2020). This technique was also adopted in the current study. The MME was used to project the ADR basin's climate change for two periods; the near future (2020-2059) and the far future (2060-2099). Spatial distribution maps were created to show the change in the basin for precipitation (%) and temperature (⁰C). Also, the change that happened in each climate zone were presented for each variable.
4.1. Mean annual precipitation spatial distribution
The mean annual precipitation climatology of CPC and 19 GCMs of CMIP6 for the period of 1979-2014 are presented in Figure 2, while the mean Tmx and Tmn were shown in S-1 and S-2 in the supplementary. CPC showed that the mean annual precipitation for the study area ranged between 50 and 350 mm. The highest precipitation occurs in the center part, while the lowest is in the basin's northwest. The precipitation decreases gradually from south of Tajikistan and north of Afghanistan until it reaches the basin's northwest. It was observed from the figure that some GCMs such as EC-Earth3-Veg and EC-Earth3-Veg-LR shows similar spatial pattern to observed precipitation. Many GCMs like ACCESS-CM2, ACCESS-ESM1-5 and BCC-CSM2-MR showed high precipitation in the center and east part of the basin, while INM-CM4-8 and INM-CM5-0 showed relatively less precipitation in the center of the basin.
4.2. Performance evaluation of GCMs
The GCM's performance was evaluated against CPC precipitation, Tmx and Tmn data for the period 1979-2014 using the KGE metric. The results are presented using a level plot in Figure 3. The median of KGE for all GCMs for annual Pr, Tmx and Tmn were -0.03, 0.51 and -1.37, respectively. Both Pr and Tmn gave a negative KGE value for many GCMs, while GCMs’ Tmx showed a high ability to replicate the CPC Tmx. The results showed the highest KGE value (0.85) for INM-CM4-8 in Tmx and the lowest, 0.11, for EC-Earth3-Veg for Pr.
4.3. Ranking of GCMs
Based on KGE values for each GCM, the GCMs were ranked for each variable in ascending order from the highest KGE to the lowest value. The higher value of KGE for a GCM indicates its better skill in replicating historical observations. The average rank of all three variables was obtained using Equation 3, as shown in Table 1. Finally, the lowest average rank was taken as the best GCM in reproducing the CPC dataset. The results indicate that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR ranked 1st, 2nd, 3rd, and 4th, respectively. The rest of the GCMs showed weak performance in replicating the observations. The top four GCMs were taken as an ensemble mean to represent the observed data for climate projection.
|
GCMs |
Rank (Pr) |
Rank (Tmx) |
Rank (Tmn) |
Average Sum |
MCDMA |
|---|---|---|---|---|---|
|
ACCESS-CM2 |
7 |
12 |
7 |
8.67 |
6 |
|
ACCESS-ESM1-5 |
19 |
6 |
1 |
8.67 |
7 |
|
AWI-CM-1-1-MR |
4 |
8 |
12 |
8 |
4 |
|
BCC-CSM2-MR |
14 |
7 |
17 |
12.67 |
16 |
|
CanESM5 |
11 |
9 |
9 |
9.67 |
10 |
|
CMCC-CM2-SR5 |
8 |
17 |
6 |
10.33 |
11 |
|
CMCC-ESM2 |
9 |
5 |
4 |
6 |
2 |
|
EC-Earth3 |
3 |
13 |
19 |
11.67 |
15 |
|
EC-Earth3-Veg |
1 |
15 |
15 |
10.33 |
12 |
|
EC-Earth3-Veg-LR |
2 |
16 |
16 |
11.33 |
14 |
|
FGOALS-g3 |
16 |
19 |
8 |
14.33 |
17 |
|
GFDL-ESM4 |
13 |
14 |
18 |
15 |
18 |
|
INM-CM4-8 |
5 |
1 |
14 |
6.67 |
3 |
|
INM-CM5-0 |
12 |
2 |
11 |
8.33 |
5 |
|
IPSL-CM6A-LR |
18 |
18 |
10 |
15.33 |
19 |
|
MIROC6 |
15 |
10 |
3 |
9.33 |
9 |
|
MPI-ESM1-2-HR |
10 |
4 |
13 |
9 |
8 |
|
MPI-ESM1-2-LR |
6 |
3 |
2 |
3.67 |
1 |
|
MRI-ESM2-0 |
17 |
11 |
5 |
11 |
13 |
4.4. Climate projection
The top selected GCMs were merged to create the mean MME. The MME was used to project the changes in annual Pr, Tmx and Tmn for four SSPs in the near future (2020-2059) and the far future (2060-2099) over different climate zones of the basin. The map of climate zones is presented in Figure 4, which was adapted from the Köppen-Geiger climate classification global map (Peel et al., 2007). The following sections summarize the projected fluctuations in annual Pr, Tmx and Tmn.
4.4.1. Precipitation projection
The changes for Pr in the ADR basin projected by MME of the selected GCMs are presented in Figure 5. Results indicate that projected change in the far future is higher (3.3-12.5 %) than in the near future for all SSPs. The percentage of change in Pr in the near future for SSP1-2.6 to SSP5-8.5 were in the range of -12.4 to 3.1%, -7.3 to 10.5%, -6.4 to 7.0% and -10.3 to 4.2%. The projected change in Pr for the far futures for those SSPs were -8.3 to 3.3%, 1.6 to 9.1%, -4.2 to 12.2% and -4.3 to 10.8%. Most GCMs projected higher Pr over a small patch in the northwest cold desert climate region. This increase in Pr could be due to the effect of atmospheric circulation of the Caspian Sea due to increased temperature (Farley Nicholls and Toumi, 2014). It could also be due to the effect of the Aral Sea surrounding the delta of the ADR basin. The highest precipitation in the east part of the basin may be due to the Siberian monsoon in winter and spring that fall as rain and snow (Wang et al., 2020). From SSP1-2.6 for the near future, it can be seen that the rainfall change is negative in most parts of the basin with harsh change in the center to south part. However, this scenario shows the negative change for the far future over the basin but relatively less compared to that in the near future.
4.4.2. Temperature projection
The annual Tmx changes were presented as the absolute difference between the future projection and the historical simulation. The geographical distribution in the changes of annual Tmx is presented in Figure 6. The figure indicates a gradual increase in Tmx from SSP1-2.6 to 5-8.5 across the basin for both the future periods. However, all GCMs showed a greater increase for the far future compared to the near future. Only SSP3-7.0 showed a homogeneous spatial distribution of Tmx over the whole basin. The lower projected change in Tmx in the near future was from 1.3 to1.6⁰C, while the far future ranged between 1.6 and 4.9⁰C. The highest increase (4.9⁰C) was for far future SSP5-8.5.
The changes in annual Tmn were very similar to Tmx. The spatial patterns of the projected change in Tmn are presented in Figure 7. Compared to Tmx, the projected change in Tmn was higher. There was a variation in change for different GCMs, except for SSP5-8.5 in the near future, which showed a more homogenous spatial pattern in change for all GCMs. Like Tmx, a gradual increase in Tmn was noticed from SSP1-2.6 to 5-8.5 across the basin for both future scenarios. The higher increase was projected for the far future in the range of 2.3−5.5⁰C. The highest increase in Tmn (5.5⁰C) was for SSP5-8.5 in the far future, while the lowest change (1.3⁰C) was for near future SSP1-2.6.
4.5. Uncertainty in projected climate change
The uncertainty in the projected climate was estimated from the lower and upper band values of projections by different GCMs for individual SSP. The uncertainty in the projected Pr in different climate zones for different SSPs are shown in Figure 8. It shows a higher uncertainty in projection in the far future (2060-2100) compared to the near future (2020-2059). The uncertainty in projection was also higher for higher SSPs. For example, the highest upper band value (32%) was observed for far future SSP5-8.5.
Uncertainties associated with Tmx are presented in Figure 9. Uncertainty in Tmx was less compared to Pr. The variation in projection was in the range of 3.0 to 7.0ºC or 4.0ºC for climate zone 4 (Warm Dry Summer Continental Climate). Similarly, the uncertainty in the projected Tmn (Figure 10) was less. The highest uncertainty in Tmn was 3.8ºC for SSP5-8.5 during 2060-2099 for climate zone 1 (Cold Desert Climate). However, like Pr, the uncertainty for both Tmx and Tmn was higher for the far future and higher SSPs.
Different GCMs models have a degree of uncertainty (Khan et al., 2020; Lutz et al., 2016). Therefore, it is important to select GCMs based on their performance in modeling the climatic of an area to decrease the uncertainty of the climate projection (Ahmed et al., 2019b; Salman et al., 2018). MMEs are usually suggested to decrease the uncertainties related to GCMs simulations or projections (Ahmed et al., 2020). In this study, we used the past performance approach to choose a subset of GCMs (MME) to reduce uncertainties related to the individual model.
Few studies have been conducted in the ADR basin, which is different from our work in terms of method and data availability. A recent study (Guo et al., 2021) used CMIP6 to simulate precipitation over CA found that the performance of GCMs varies from region to region with a consistent annual cycle shape in western CA. They showed limited models performance in simulating the observations. White et al. (2014) used mean MME SRA2 precipitation annual temperature anomalies for 2070-2099 over the ADR basin. They showed a mean annual rise in temperature ranged between 4 and 4.6°C through the basin by 2070-2099 under a high-emission scenario and downward precipitation trend, ranging between −5.3% in Nukus city Uzbekistan and -13.6% in Mary city in the Karakum desert Turkmenistan. Xu et al. (2021) projected streamflow in ADR for climate change scenarios. The multi-scenario ensemble streamflow forecast showed that during 2021-2050, the mean annual precipitation and the mean temperature should increase. However, the average annual streamflow would decline, which climate change donates 78.8–98.7% of streamflow change in each scenario.
This study used the past performance approach to select a subset of GCMs from 19 GCMs. Secondly, the KGE metric was used to assess the association between all GCMs and the CPC (Pr, Tmx and Tmn) at each of the 73 grid points of 1°×1° resolution. Thirdly, an MCDMA was used to rank the inconsistent results obtained using KGE for three variables (Pr, Tmx and Tmn). The results of MCDMA revealed that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR are the best in replicating observed climate in ADRB.
Results obtained using future MMEs showed an increase in Pr ranged between 3.3 and 12.5% in the far future (2060-2099) when all SSPs were considered. The four SSPs' spatial pattern of projected change showed a higher increase in Pr in the east and northwest of the basin and less in the basin's centre. This result contradicts Agal’tseva et al. (2010) findings, which showed a 5-12% reduction in precipitation during 2070-2099.
The absolute change in temperature between the historical and future projection MME were used to present the change in Tmx and Tmn. The results indicated a gradual increase in Tmx and Tmn in the basin. The results were almost in agreement with that reported in previous studies (Agal’tseva et al., 2010; Gulakhmadov et al., 2020; White et al., 2014; Xu et al., 2021) in the region.
The performance of 19 GCM-CMIP6 in replicating the historical precipitation and temperature in the ADR basin was assessed to recommend an appropriate set of GCMs for climate studies. Besides, the study employed the selected GCMs for the projection of the climate of the basin for different SSPs with associated uncertainty. The results showed that MPI-ESM1-2-LR, CMCC-ESM2, INM-CM4-8 and AWI-CM-1-1-MR are the best in replicating observed Pr, Tmx and Tmn in the ADR basin. Climate projection using the selected GCMs revealed a rise in Pr, Tmx and Tmn in the basin for all SSPs, especially for 2060-2099. A rise in both precipitation and temperature was projected higher for higher SSPs and more in the far future compared to the near future. The uncertainty in projected climate showed more uncertainty in precipitation projections compared to temperature. The uncertainty was also noticed higher for higher SSPs and in the far future compared to the near future. This is the first attempt to evaluate CMIP6 GCMs’ performance in the ADR basin to select the GCM ensemble and climate projections using the selected ensemble. The results of the study can be used for climate change studies and decision-making processes. In the future, GCMs' skills can be evaluated according to the ability to replicate climatic extremes in the basin. Besides, the selected GCMs can be used to evaluate future changes in climate extremes, like floods and droughts in the basin.
Acknowledgement
The authors are grateful to Universiti Teknologi Malaysia (UTM) for providing financial support to conduct this research through grant No Q.J130000.2451.09G07. The authors are thankful to the Climate Prediction Center (CPC) of NOAA, USA and WCRP Coupled Model Intercomparison Project (Phase 6) website of Program for Climate Model Diagnosis & Intercomparison (PCMDI) for providing gridded precipitation data through their data portal.
Funding
Author(s) are grateful to Universiti Teknologi Malaysia (UTM) for supporting this research through grant no. Q.J130000.2451.09G07
Conflict of interest
The authors declare no conflict of interest.
Availability of data:
All the data are available in the public domain at the links provided in the texts
Availability of code
The codes used for the processing of data can be provided on request to the corresponding author.
Ethics approval
Not Applicable
Consent to participate
Not Applicable
Consent for publication
All the authors consented to publish the paper
Authors contribution
All the authors contributed to conceptualizing and designing the study. Obaidullah Salehie and Tarmizi bin Ismail gathered data; the programming code was written by Shamsuddin Shahid and Mohammed Magdy Hamed; the initial draft of the paper was prepared by Obaidullah Salehie and Tze Huey Tam; the article was repeatedly revised to generate the final version by Tarmizi bin Ismail and Shamsuddin Shahid.
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